A5019105284- Matrix Theory and Algorithms
- Image and Signal Denoising Methods
- EEG and Brain-Computer Interfaces
- Neural dynamics and brain function
- Electromagnetic Scattering and Analysis
- Neural Networks and Applications
- Earthquake Detection and Analysis
- Geomagnetism and Paleomagnetism Studies
- Functional Brain Connectivity Studies
- Sparse and Compressive Sensing Techniques
- Ionosphere and magnetosphere dynamics
Nanjing University of Information Science and Technology
2023-2025
Xi'an Jiaotong University
2022-2023
Ministry of Civil Affairs
2022
Linear discriminant analysis (LDA) faces challenges in practical applications due to the small sample size (SSS) problem and high computational costs. Various solutions have been proposed address SSS both ratio trace LDA (TRLDA). However, iterative processing of large matrices often makes computation process cumbersome. To this issue, for TRLDA, we propose a novel random method that extracts orthogonal bases from matrices, allowing computations with small-sized matrices. This significantly...
Monitoring the consciousness states of patients and ensuring appropriate depth anesthesia (DOA) is critical for safe implementation surgery. In this study, a high-density electroencephalogram (EEG) combined with blood drug concentration behavioral response indicators was used to monitor propofol-induced sedation evaluate alterations in states. Microstate analysis, which can reflect semi-stable state sub-second activation brain functional network, be assess brain's research, EEG microstate...
This perspective focuses on the fast algorithm design for singular value decomposition and inverse computation of nearly low-rank matrices that are potentially big sizes.
Low-rank matrix approximation play a ubiquitous role in various applications such as image processing, signal and data analysis. Recently, random algorithms of low-rank have gained widespread adoption due to their speed, accuracy, robustness, particularly improved implementation on modern computer architectures. Existing often require prior knowledge the rank matrix, which is typically unknown. To address this bottleneck, we propose algorithm termed efficient orthogonal decomposition with...